Data Scientist, Growth Product

<h2><span style="font-family: helvetica, arial, sans-serif;"><strong>About the role</strong></span></h2> <p class="p2">We're looking for a Growth Product Scientist to partner with our Growth Product and Engineering teams to drive measurable impact across the member journey — from acquisition to conversion through long-term retention. You'll turn data into the decisions that shape how millions of members discover, direct deposit, and get value from Chime.</p> <p class="p2">This role sits at the intersection of product, data, and experimentation. You'll own funnels end-to-end, design and analyze A/B tests, and translate ambiguous business questions into clear, prioritized recommendations that ship.</p> <p class="p3">In this role, you will work closely with product managers, design, engineers, product & lifecycle marketing, and operational stakeholders to foster a data-driven product development culture, advise our product roadmaps, and build a deep understanding of member behavior.</p> <p class="p3">The base salary offered for this role and level of experience will begin at $109,000 and go up to $150,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.</p> <h2><span style="font-family: helvetica, arial, sans-serif;"><strong>In this role, you can expect to</strong></span></h2> <ul class="ul1"> <li class="li3">Partner with Product Managers, Engineers, Designers, and Marketers on growth initiatives spanning acquisition, onboarding, activation, and retention</li> <li class="li3">Design, run, and analyze A/B tests to improve member product experiences, including metric creation, experiment design, power analysis, and analysis of experiment results.; develop frameworks to prioritize the highest-leverage experiments</li> <li class="li3">Drive data-informed decision making within Growth org by equipping PMs and engineers with self-service analytics tools, and conducting ad hoc analyses and causal studies for the team.<span class="Apple-converted-space"> </span></li> <li class="li3">Use advanced statistical methods for causal inference, as well as time-series and other forecasting techniques, to solve product questions for the team. Occasionally apply machine learning methods for problems such as customer segmentation.<span class="Apple-converted-space"> </span></li> <li class="li3">Build and maintain dashboards, KPIs, and self-serve tooling that give the team a clear view of funnel health</li> <li class="li3&quo